code: def trajectory_fusion(
      mot_feature, cid, cid_bias, use_zone=False, zone_path=""
  ):
      cur_bias = cid_bias[cid]
      mot_list_break = {}
      if use_zone:
          zones = Zone(zone_path=zone_path)
          zones.set_cam(cid)
          mot_list = parse_pt(mot_feature, zones)
      else:
          mot_list = parse_pt(mot_feature)
  
      if use_zone:
          mot_list = zones.break_mot(mot_list, cid)
          mot_list = zones.filter_mot(mot_list, cid)  # filter by zone
          mot_list = zones.filter_bbox(mot_list, cid)  # filter bbox
  
      mot_list_break = gen_new_mot(
          mot_list
      )  # save break feature for gen result
  
      tid_data = dict()
      for tid in mot_list:
          tracklet = mot_list[tid]
          if len(tracklet) <= 1:
              continue
          frame_list = list(tracklet.keys())
          frame_list.sort()
          # filter area too large
          zone_list = [tracklet[f]["zone"] for f in frame_list]
          feature_list = [
              tracklet[f]["feat"]
              for f in frame_list
              if (tracklet[f]["bbox"][3] - tracklet[f]["bbox"][1])
              * (tracklet[f]["bbox"][2] - tracklet[f]["bbox"][0])
              > 2000
          ]
          if len(feature_list) < 2:
              feature_list = [tracklet[f]["feat"] for f in frame_list]
          io_time = [
              cur_bias + frame_list[0] / 10.0,
              cur_bias + frame_list[-1] / 10.0,
          ]
          all_feat = np.array([feat for feat in feature_list])
          mean_feat = np.mean(all_feat, axis=0)
          tid_data[tid] = {
              "cam": cid,
              "tid": tid,
              "mean_feat": mean_feat,
              "zone_list": zone_list,
              "frame_list": frame_list,
              "tracklet": tracklet,
              "io_time": io_time,
          }
      return tid_data, mot_list_break
  
file_path: PaddleDetection/deploy/pptracking/python/mot/mtmct/postprocess.py
imports: import os
  import re
  import cv2
  from tqdm import tqdm
  import numpy
  from functools import reduce
  from .utils import parse_pt_gt, parse_pt, compare_dataframes_mtmc
  from .utils import get_labels, getData, gen_new_mot
  from .camera_utils import get_labels_with_camera
  from .zone import Zone
  from ..visualize import plot_tracking
label_desc: Use image node_id 1 for calling a global function with example usage: trajectory_fusion(mot_feature, cid, cid_bias, use_zone, zone_path) and returns: tid_data,  mot_list_break